Drivers of energy efficiency for manufacturing SMEs in Eurasian countries: a profiling analysis using machine learning techniques

This study profiles manufacturing small and medium-sized enterprises (SMEs) in Eurasian countries regarding their practices of energy efficiency investments and energy management techniques. Given that the energy efficiency gap could be larger for SMEs because of the barriers identified in the related literature, the profiling of SMEs regarding their energy efficiency practices could help design specific policies that could be adopted for SMEs with a higher likelihood of insufficient energy efficiency investments. Advanced machine learning techniques, such as the random forest algorithm, enable us to perform such profiling. In profiling SMEs, the article uses the group enterprise survey collected by the European Bank for Reconstruction and Development-European Investment Bank-World Bank. The results of the random forest algorithm suggest that the most important input variable to identify the firm behavior to make an effort to enhance energy efficiency or adopt any energy management method is the sector of the firm, followed by firm size, number of skilled workers, the expertise of the top manager, and the firm’s experience. Contrary to the main findings in the literature, the firm’s ownership structure is the least important factor in forecasting its energy efficiency efforts. The elements of a clean energy strategy do not matter for efforts to enhance the energy efficiency, either. These results suggest that if policymakers in Eurasia were to design policies for manufacturing SMEs to make them invest more in energy efficiency, they should address smaller, younger enterprises with relatively less human capital when giving public subsidies.